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This article mainly introduces how to solve the problem that php can not calculate floating point numbers. It is very detailed and has certain reference value. Friends who are interested must finish reading it.

Php cannot calculate floating-point numbers because the underlying binary of the computer cannot accurately represent floating-point numbers. The solution is to use accurately calculated class libraries or function libraries, such as the BC high-precision function library in php.

This article operating environment: windows7 system, PHP7.1 version, DELL G3 computer

The problem of floating-point calculation in php

If you use the +-* / of php to calculate floating-point numbers, you may encounter some incorrect results, such as echo intval (0.58-100); it will print 57 instead of 58, which is actually a bug in which the underlying binary system of the computer cannot accurately represent floating-point numbers, which is cross-language, and I also encountered this problem with python. So basically most languages provide accurate computing class libraries or function libraries, such as php has BC high-precision function library, here are some commonly used BC high-precision functions to use.

Exampl

Why is the output 57? PHP's bug?

I believe there are many students who have such questions, because many people just ask me similar questions, not to mention people often ask me on bugs.php.net.

To understand this reason, we first need to know the representation of floating-point numbers (IEEE 754):

Floating-point numbers, taking the length of 64 bits (double precision) as an example, will be represented by 1-bit symbol bit (E), 11 exponential bit (Q), 52-bit Mantissa (M) (a total of 64 bits).

Symbol bit: the highest bit represents the positive or negative of the data, 0 represents a positive number, and 1 represents a negative number.

Exponential bit: represents the power of the data with 2 as the base, and the exponent is expressed by offset code

Mantissa: a valid number after the decimal point of the data.

The key point here is that decimals are represented in binary. About how decimals are expressed in binary, you can do it in Baidu. I'm not going to repeat them here. The key thing to understand is that 0.58 is an infinitely long value for binary representation (the following number omits the implied 1).

The binary representation of 0.58 is basically (52 bits): 0010100011101010101000011101010000101000110.57 basically (52 bits) is: 001000111101010100001010001111111101010101000010100011110, and the binary of both, if only calculated by these 52 bits, are: www.111cn.net

0.58-> 0.57999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999990.58-> 0.57999999999999999999999999999999990.58-> 0.57999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999999 0.58 * 100 = 57.999999999

Then if you intval it, it will be 57. .

It can be seen that the key point of this question is: "you seem to have a finite decimal, but it is infinite in the binary representation of the computer."

So, stop thinking that this is PHP's bug, that's what it is. ..

There is an inaccurate problem with PHP floating-point type +-*% /.

For example:

$a = 0.1; $b = 0.7; var_dump (($a + $b) = 0.8)

The printed value is boolean false

Why? the PHP manual has the following warning messages for floating-point numbers:

Warning

Floating point precision

Obviously, simple decimal fractions like 0.1 or 0.7 cannot be converted to an internal binary format without losing a little bit of precision. This can lead to confusing results: for example, floor ((0.10.7) * 10) usually returns 7 instead of the expected 8, because the internal representation of the result is actually similar to 7.9999999999.

This has to do with the fact that it is impossible to accurately express certain decimal fractions in finite digits. For example, the decimal 1 stroke 3 becomes 0.3333333. . . .

So never believe that floating-point results are accurate to the last digit, and never compare whether two floating-point numbers are equal. If you do need higher precision, you should use any precision mathematical function or gmp function

The code is as follows:

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